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Found 7 papers, 5 papers with code
DetectiveQA: Evaluating Long-Context Reasoning on Detective Novels
DetectiveQA focuses on evaluating the long-context reasoning ability of LLMs, which not only requires a full understanding of context but also requires extracting important evidences from the context and reasoning according to extracted evidences to answer the given questions.
Data Mixing Laws: Optimizing Data Mixtures by Predicting Language Modeling Performance
Pretraining data of large language models composes multiple domains (e. g., web texts, academic papers, codes), whose mixture proportions crucially impact the competence of outcome models.
AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music.
LLM can Achieve Self-Regulation via Hyperparameter Aware Generation
HAG extends the current paradigm in the text generation process, highlighting the feasibility of endowing the LLMs with self-regulate decoding strategies.
Diffusion Language Models Can Perform Many Tasks with Scaling and Instruction-Finetuning
We then reprogram pretrained masked language models into diffusion language models via diffusive adaptation, wherein task-specific finetuning and instruction finetuning are explored to unlock their versatility in solving general language tasks.
DINOISER: Diffused Conditional Sequence Learning by Manipulating Noises
In this paper, we introduce DINOISER to facilitate diffusion models for sequence generation by manipulating noises.
Energy-based Unknown Intent Detection with Data Manipulation
Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.
